class_docstr: Inference Dataset to perform prediction.
  
  Args:
      path (str | Path): Path to an image or image-folder.
      transform (A.Compose | None, optional): Albumentations Compose object describing the transforms that are
          applied to the inputs.
      image_size (int | tuple[int, int] | None, optional): Target image size
          to resize the original image. Defaults to None.
code: def __init__(
      self,
      path: str | Path,
      transform: A.Compose | None = None,
      image_size: int | tuple[int, int] | None = None,
  ) -> None:
      super().__init__()
  
      self.image_filenames = get_image_filenames(path)
  
      if transform is None:
          self.transform = get_transforms(image_size=image_size)
      else:
          self.transform = transform
  
file_path: anomalib/src/anomalib/data/inference.py
imports: from __future__ import annotations
  from pathlib import Path
  from typing import Any
  import albumentations
  from torch.utils.data.dataset import Dataset
  from anomalib.data.utils import get_image_filenames, get_transforms, read_image
label_desc: Use image node_id 1 to create a new InferenceDataset object from inherited base classes: Dataset with example: obj = InferenceDataset(path, transform, image_size)